1. Technical Field
The present disclosure relates to image denoising and, more specifically, to a system and method for image denoising optimizing object curvature.
2. Discussion of Related Art
Noise reduction, or denoising, is the process of removing noise from a signal such as a digital image signal. Noise may be introduced into an image signal both at the time of acquisition, for example, due to the limitations of the image-acquiring device, or at any point thereafter. It is often desirable to remove noise from an image. For example, in the case of medical images, such as computed tomography (CT) images and magnetic resonance imaging (MRI), noise may interfere with the diagnostic value of the medical image. While denoising may increase the diagnostic value of medical images, it is also possible that suboptimal denoising techniques may inadvertently remove actual image features and reduce the diagnostic value of medical images to some degree.
Image denoising also has uses beyond processing of medical images. For example, photographic images may also be susceptible to noise and denoising may be used to enhance these images as well.
Accordingly, image denoising and/or filtering may be a preliminary step in many image processing schemes. Image denoising may be used to remove artifacts associated with image acquisition and other imaging problems.
One simple approach to denoising is to replace the color of a pixel with an average of nearby pixel colors. Such an approach may be particularly effective where there is some reason for neighboring pixels to share color values. However, such an approach may also create a blurring effect at points of the image where intensity change is abrupt. This phenomena may be particularly apparent for images including geometric patterns.
Non-local means denoising is another approach to image denoising in which rather than assuming that pixels share commonality with adjacent pixels, the image is instead searched for other areas that are similar in appearance to the area currently being denoised. Then, the pixel value for the pixel being denoised is replaced with an average pixel value for the most similar areas.
Many existing approaches to image denoising seek to minimize an energy function that generally combines a prior data fidelity term with a regularization term encoding a model of image structure. For example, total variation (TV) has been commonly used as an energy function that may be minimized in performing image denoising. Such approaches may be referred to herein as TV denoising.